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Building AI Applications on Google Cloud Platform

Author: ziuziu on 1-11-2019, 11:23, views: 202

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Building AI Applications on Google Cloud Platform
MP4 | Video: AVC 1280x720 | Audio: AAC 48 KHz 2 | Duration: 03:27:52 | 6.3 GB
Genre: eLearning | Language: English

There is a rapid evolution occurring in machine learning with tools like AutoML that basically automate many of the tedious aspects of machine learning and allow developers to focus on getting results into production. Noah Gift illustrates just now to harness this technology and deploy it successfully on Google Cloud Platform, demonstrating for developers how to employ the current best practices and automated tools to create analytics applications that solve real-world problems.
Developers who want to take their Data Science skills to the next level and build AutoML applications in the Cloud will benefit from this unique course, as they learn how to use AutoML, Big Query, Python, and Google App Engine to create sophisticated AI.
Description
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google's state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML's graphical user interface to train, evaluate, improve, and deploy models based on their data.
This LiveLesson covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de facto standard language for AI application development in the cloud.
About the Instructor
Noah Gift is a lecturer at the UC Davis Graduate School of Management, MSBA program, the Graduate Data Science program, MSDS, at Northwestern and the Graduate Information and Data Science Program at UC Berkeley. He is teaching and designing graduate machine learning, AI, Data Science, and Cloud Architecture courses. These responsibilities include leading a multi-cloud certification initiative for students.
Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Certified Big Data-Specialist, AWS Certified Machine Learning-Specialist, AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, Microsoft MTA on Python.
Noah was selected to the SME Machine Learning team due to accomplishments in the area of Machine Learning on the AWS platform. He has published close to 100 technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, an M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently, he is consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson, 2018).
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